Alzheimer's Disease

Automatic lymphedema segmentation in T2-STIR MRI using an unsupervised clustering method.

PURPOSE: To develop and evaluate an unsupervised artificial intelligence (AI)-based method for the automated segmentation and quantitative assessment of fluid content on T2 short tau inversion recovery (STIR) magnetic resonance lymphography (MRL) in patients with lymphedema and lipolymphedema. METHODS: A heterogeneous cohort of 20 patients with lymphedema or lipolymphedema was retrospectively sele...

Towards a speech-based digital biomarker for cognitive impairment: speech as a proxy for cognitive assessment.

With the growing prevalence of cognitive decline in ageing populations, accessible and scalable screening tools are essential for early intervention. This study investigated the potential of automated speech analysis as a proxy for cognitive assessment in 1003 older adults. Employing machine learning regression models, we demonstrated that linguistic and acoustic features extracted from spontaneou...

Multi-omics mapping identifies CYBA-mediated mitochondrial dysfunction driving macrophage polarization and ferroptosis via Nrf2 pathway in atherosclerosis.

Atherosclerosis (AS), a chronic inflammatory process driven largely by macrophage-mediated plaque formation, remains poorly understood in mitochondria...

Smart assistive technologies for neurodisorders: A review on AI, IoT, and wearable systems for enhanced patient care.

Neurological disorders of the brain and spinal cord affect millions of individuals worldwide and continue to rise in prevalence. Conditions such as Al...

Alzheimer's disease diagnosis support for brain perfusion SPECT scans in a real-world clinical cohort.

BackgroundDementia diagnosis is challenging and often delayed. Brain imaging techniques such as single-photon emission computed tomography (SPECT) ima...

The role of dentists in the recognition of neurodegenerative and systemic conditions with neurological involvement.

Dentists are often the first healthcare providers to observe subtle orofacial and behavioral changes that may reflect underlying neurological diseases...

Childhood dyslexia risk elevated by heavy metal mixtures from e-waste: A machine learning-driven mixture modeling study.

Environmental heavy metal mixtures from informal e-waste recycling are potential neurotoxicants, but their link to developmental dyslexia remains uncl...

Molecular features of human pathological tau distinguish tauopathy-associated dementias.

In Alzheimer's disease (AD), pathological tau protein shows a progressive accumulation of post-translational modifications (PTMs), reflecting disease ...

Optimizing Early Detection of Diabetic Kidney Disease through Synergistic Biomarkers and Serum Metabolites in Human.

BACKGROUND: Diabetic kidney disease (DKD) progresses to end-stage renal disease more rapidly than chronic kidney disease due to persistent hyperglycem...

Development and Validation of a Machine Learning Model for Dementia Staging in a Heterogeneous Cognitive Impairment Cohort Using Multimodal Features.

RATIONALE AND OBJECTIVES: With the emergence of disease-modifying therapies, precise staging of dementia is urgent. This study aimed to develop a mach...

Rationalizing nose-to-brain drug delivery: Machine learning-guided optimization and mechanistic elucidation of olfactory deposition for nasal sprays.

Nose-to-brain drug delivery via nasal sprays is severely limited by low olfactory deposition. In this study, we integrate machine learning with high-t...

Musicality in protein interaction dynamics informs the multi-scale evolution of prosocial behaviour.

Like animal vocalization and display, human singing and dancing allows non-verbal establishment of behavioural co-relation (i.e. correlation) between ...

Towards the development of a management protocol for subjective cognitive decline: Insights from a cross-sectional and longitudinal analysis of multimodal data from a memory clinic.

BackgroundSubjective cognitive decline (SCD) represents the first early symptomatic stage of Alzheimer's disease (AD).ObjectiveWe aimed to investigate...

How reproducible are data-driven subtypes of Alzheimer's disease atrophy?

BackgroundAlzheimer's disease (AD) exhibits substantial clinical and biological heterogeneity, complicating efforts in treatment and intervention deve...

Neuroimmune Cross-Talk and Multilevel Cascades in Fentanyl Toxicity: Interplay of Hypoxic Stress, Glial Activation, and Synaptic Dysregulation in Systems-Level Neurodegeneration.

Fentanyl, an ultra-potent synthetic opioid, has traditionally been characterized by its acute toxic effects, particularly respiratory depression. Howe...

Ethical Knowledge, Challenges, and Institutional Strategies Among Medical AI Developers and Researchers: Focus Group Study.

BACKGROUND: As artificial intelligence (AI) becomes increasingly embedded in clinical decision-making and preventive care, it is urgent to address eth...

Correlation between oral disease and neurodegenerative disorders: Role of biological proteins for the modulation of oral-brain axis and gut-brain axis.

Biological proteins play a crucial role at the intersection of oral health and neuroscience, offering promising opportunities for improved diagnosis, ...

Dementia severity index: A threshold-based approach to classifying dementia levels using resting state EEG.

BACKGROUND: Alzheimer's Disease (AD) and FrontoTemporal Dementia (FTD) are dementia conditions that often overlap clinically, leading to misdiagnoses....

Converse or reverse? Machine-learning modeling for disease progression: A study based on Alzheimer's disease continuum cohort.

INTRODUCTION: Longitudinal trajectories from healthy aging to Mild Cognitive Impairment and Alzheimer's Disease involve complex mechanisms. METHODS: W...

Generating synthetic MRI scans for improving Alzheimer's disease diagnosis.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia. Magnetic Resonance Imaging (MRI) combined with...